1. Field of the Invention
This invention relates generally to an automated method and system for detecting, classifying and displaying abnormal anatomic regions, particularly individual and clustered microcalcifications, lesions, parenchymal distortions, interstitial lung disease, etc. existing in digital medical images, such as mammograms and chest radiographs.
2. Discussion of Background
Detection and diagnosis of abnormal anatomical regions in radiographs, such as cancerous lung nodules in chest radiographs and microcalcifications in women's breast radiographs, so called mammograms, are among the most important and difficult task's performed by radiologists. [1-27]
Recent studies have concluded that the prognosis for patients with lung cancer is improved by early radiographic detection. In one study on lung cancer detection, it was found that, in retrospect, 90% of subsequently diagnosed peripheral lung carcinomas were visible on earlier radiographs. The observer error which caused these lesions to be missed may be due to the camouflaging effect of the surrounding anatomical background on the nodule of interest, or to the subjective and varying decision criteria used by radiologists. Underreading of a radiograph may be due to a lack of clinical data, lack of experience, a premature discontinuation of the film reading because of a definite finding, focusing of attention on another abnormality by virtue of a specific clinical question, failure to review previous films, distractions, and "illusory visual experiences."
Similarly, early diagnosis and treatment of breast cancer, a leading cause of death in women, significantly improves the chances of survival. X-ray mammography is the only diagnostic procedure with a proven capability for detecting early-stage, clinically occult breast cancers. Between 30 and 50% of breast carcinomas detected radiographically demonstrate microcalcifications on mammograms, and between 60 and 80% of breast carcinomas reveal microcalcifications upon microscopic examination. Therefore any increase in the detection of microcalcifications by mammography will lead to further improvements in its efficacy in the detection of early breast cancer. The American Cancer Society has recommended the use of mammography for screening of asymptomatic women over the age of 40 with annual examinations after the age 50. For this reason, mammography may eventually constitute one of the highest volume X-ray procedures routinely interpreted by radiologists.
A computer scheme that alerts the radiologist to the location of highly suspect lung nodules or breast microcalcifications should allow the number of false-negative diagnoses to be reduced. [28-42, 45-51, 53-56, 58-60, 63-70, 105] This could lead to earlier detection of primary lung and breast cancers and a better prognosis for the patient. As more digital radiographic imaging systems are developed, computer-aided searches become feasible.
Successful detection schemes could eventually be hardware implemented for on-line screening of all chest radiographs and mammograms, prior to viewing by a physician. Thus, chest radiographs ordered for medical reasons other than suspected lung cancer would also undergo careful screening for nodules.
Several investigators have attempted to analyze mammographic abnormalities with digital computers. However, the known studies failed to achieve an accuracy acceptable for clinical practice. This failure can be attributed primarily to a large overlap in the features of benign and malignant lesions as they appear on mammograms.
The currently accepted standard of clinical care is such that biopsies are performed on 5 to 10 women for each cancer removed. Only with this high biopsy rate is there reasonable assurance that most mammographically detectable early carcinomas will be treated. Given the large amount of overlap between the characterization of abnormalities may eventually have a greater impact in clinical care. Microcalcifications represent an ideal target for automated detection, because subtle microcalcifications are often the first and sometimes the only radiographic findings in early, curable, breast cancers, yet individual microcalcifications in a suspicious cluster (i.e., one requiring biopsy) have a fairly limited range of radiographic appearances.
One of the early steps in a computer-aided system is to segment a digitized radiographic image, such as a mammogram, into foreground, for example, corresponding to the breast and background, for example, corresponding to the external surroundings of the breast (see, e.g., U.S. Pat. No. 5,452,367.) This segmentation reduces the amount of further processing because extraneous pixels belonging to the background are removed from further consideration. Also, the boundary contour or border between the foreground and the background, theoretically at the skinline, is ascertained. Next, a search for masses within the area segmented as corresponding to the breast may be accomplished by analyzing the size and shape of spots, sometimes referred to as "blobs" or "islands", that are discriminated by thresholding the mammogram at one or a few intensity levels. For example, in U.S. Pat. No. 5,212,637, a search for masses in different intensity ranges utilizes a calculated initial threshold value which threshold value is incremented no more than three times "Blobs" produced by thresholding the mammogram at the initial or at an incremented threshold value, which correspond to regions having a sufficient prominence in intensity with respect to their immediate surround are classified as "potentially malignant" based on their size and shape, i.e. area, circularity, and eccentricity (see, also, U.S. patent application Ser. No. 08/515,798 now U.S. Pat. No. 5,832,103.)
The inventors and others at the Radiology Department at the University of Chicago have been developing a computerized scheme for the detection of clustered microcalcifications in mammograms with the goal of assisting radiologists' interpretation accuracy. (See H. P. Chan et al., "Image feature analysis and computer-aided diagnosis in digital radiography. 1. Automated detection of microcalcifications in mammography," Med. Phys. 14, 538-548 (1987); H. P. Chan et al., "Computer-aided detection of microcalcifications in mammograms: Methodology and preliminary clinical study," Invest Radiol. 23, 664-671 (1988); H. P. Chan et al., "Improvement in radiologists' detection of clustered microcalcifications on mammograms: The potential of computer-aided diagnosis," Invest Radiol. 25, 1102-1110 (1990); R. M. Nishikawa et al., "Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms," Proc. SPIE 1905, 422-432 (1993); and R. M. Nishikawa et al., "Computer-aided detection of clustered microcalcifications: An improved method for grouping detected signals," Med. Phys. 20, 1661-1666 (1993).)
The computer outputs from this scheme, which involves quantitative analysis of digitized mammograms, indicate possible locations of clustered microcalcifications. These locations can be marked by arrows superimposed on mammograms displayed on the monitor of a workstation. (See U.S. Pat. No. 4,907,156.) If the computer output is presented to radiologists as a "second opinion" (see K. Doi et al., "Digital radiography: A useful clinical tool for computer-aided diagnosis by quantitative analysis of radiographic images," Acta Radiol 34, 426-439 (1993); and M. L. Giger, "Computer-aided diagnosis," RSNA Categorical Course in Physics, 283-298 (1993)), it is expected that the accuracy in detecting clustered microcalcifications in mammograms would be improved by reducing false-negative detection rate. The prior computer-aided diagnosis (CAD) scheme has a sensitivity (i.e., to include as many true microcalcifications as possible) of approximately 85% with 0.5 false-positive clusters per mammogram. Since the sensitivity is at a relatively high level, a reduction of false-positive detection rate is desired before beginning clinical testing. The prior scheme uses the first moment of the power spectrum and the distribution of microcalcification signals to eliminate false-positive microcalcification signals. To reduce further the false-positive rate, new techniques, including application of an artificial neural network (see U.S. Pat. Nos. 5,463,548, 5,491,627, 5,422,500, 5,622,171 and 5,732,697 and pending U.S. patent application Ser. No. 08/562,087) and an area-thickness analysis (see Y. Jiang et al., "Method of extracting microcalcifications' signal area and signal thickness from digital mammnograms," Proc SPIE 1778, 28-36 (1992)) have been investigated and have been shown to be effective.
Differential diagnosis of interstitial lung disease is one of the major subjects in chest radiology (see U.S. Pat. Nos. 4,839,807, 5,289,374, 5,319,549, 5,343,390, and 5,638,458 and pending U.S. patent application Ser. No. 08/758,438.) It is also a difficult task for radiologists because of the similarity of radiological findings on chest radiographs and the complexity of clinical parameters. Artificial neural networks (ANNs) have been applied using hypothetical cases for differential diagnosis of interstitial lung disease and showed the potential utility of ANNs (see, e.g., Asada et al., "Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung disease: pilot study," Radiology 1990, 177:857-860, and U.S. Pat. No. 5,622,171, and pending U.S. patent application Ser. Nos. 08/562,087, and 08/758,438.) However, no testing has been performed with actual clinical cases along with hypothetical cases.
Computer-aided diagnosis (CAD), a diagnosis made by a radiologist who considers the results of a computerized analysis of the radiograph when making his/her decision, has been proposed as a means of improving radiologists' ability to detect and diagnose disease. However, in order for CAD to be effective, clinically, the computerized techniques must be sufficiently accurate to aid the radiologist, and the computer results need to be conveyed to the radiologist in a meaningful and easy-to-use manner (see, e.g., pending U.S. patent application Ser. No. 08/757,611.)
There are generally two different types of CAD techniques being developed. One is for the detection of abnormalities, where the computer identifies suspicious areas (ROIs) in the radiograph. The other is quantification of the an area of an image, for example classifying a lesion as benign or malignant. Here the task is not to find suspicious areas, but rather to provide some quantitative assessment of the area to assist the radiologist in making a diagnosis or recommending patient treatment.
However, further improvement in detecting, classifying and displaying abnormal anatomic regions, particularly individual and clustered microcalcifications, lesions, parenchymal distortions, interstitial lung disease, etc. existing in digital medical images, such as mammograms and chest radiographs is desired.